1. Field of Invention
The present invention relates to the field of predicting events, and particularly risks such as earthquakes. The invention is more particularly related to a comprehensive statistical analysis of past and current trigger factors and other conditions that allow an accurate prediction of an event.
2. Discussion of Background
The capacity to respond to the world's natural disasters is becoming increasingly more difficult, both in terms of insurance and mitigation, and many countries will be on their own to respond to these events. While many governments are struggling to identify their hazards and implement long-term strategies to prepare for them, there is a tremendous need for short-term forecasting to minimize casualties and losses.
Economic losses in the nineties amounted to over $535 billion ($99 billion insured) while in the fifties losses were roughly $38 billion (with less than $5 billion insured). The change over this period is an increase in losses by a factor of 14.
Individual earthquakes may also have extreme repercussions on national economies and in some cases contribute to a significant drop in national GDP, such as with Algeria or Armenia.
Underwriting losses parallel the frequency and severity of property catastrophes and overall the industry is writing business at an underwriting loss. Operating margins remain weak for a number of companies resulting from not only substantial losses due to catastrophes, but also from the price wars sustained in the early 90's.
The increased losses to natural disasters are largely due to the increased migration of people into great population centers. Of 100 of the largest cities of the world, with populations exceeding 2 million, over 40 lie within 200 km of a plate boundary. Many of these cities are classified by the United Nations as mega-cities with populations over 8 million, and further growth is aggravating the urban long-term seismic risk. The problem is particularly acute for those cities that are also capital cities, drawing significant proportions of their population into a single urban center such as Mexico City (24%), Santo Domingo (35%), Athens (37%), Tel Aviv (42%), Lima (31%), Santiago (36%), Wellington, New Zealand (13%) and many others.
Earthquake activity has typically been modeled as a random or Poisson process. Most earthquake activity appears to follow the well known Gutenburg-Richter magnitude frequency relationship, log(N)=a−bM, where N is the number of earthquakes, a is a constant that specifies the level of seismic activity, b is a constant that indicates the rate of activity and M is the magnitude.
The traditional and current status of earthquake forecasting lies in modeling the rate of earthquake activity on a particular fault or region to come up with long term probabilities of activity. Results are non-specific and often stated as “there is a 20% chance of a magnitude 7 earthquake in 50 years”.
This modeling of probability of earthquake occurrence involves determining the average activity on known faults, incorporating the activity on unknown nearby faults and calculating the probability of future activity using time-independent Poissonian probabilities as well as time-dependent probabilities as faults progress through the “earthquake cycle”.
In terms of actual forecasting, the widely used approach to short term earthquake forecasts is limited to aftershocks. Once a large mainshock earthquake occurs, statements about the expected aftershocks are limited to an area near or surrounding the fault and the size of the potential aftershocks.
Although most seismologists and engineers model earthquake activity as a random process, it is also well known that earthquakes may either cluster in time or may be induced through human or natural intervention, neither of which follows a random process.
Examples of clustering include earthquake swarms, multiple large events spaced closely in time and aftershock series.
Examples of induced seismicity include earthquakes associated with the filling or emptying of reservoirs, or the injection of fluids at depth such as in Colorado in the early sixties. Triggering by natural solid earth phenomena includes distant earthquakes as triggering sources and earth tides.
Numerous other factors correlate with earthquake activity, including seismicity related to weather related phenomena such as El Niño, oceans storms (e.g. sea waves beating on the shore), vertical loading of the earth's crust by the atmosphere, and horizontal loading of the earth's crust by the atmosphere.
Finally, there are strong correlations between earthquake activity and the occurrence of space weather phenomena. Seismicity rates have been linked to solar flares and its byproducts as well as solar periodicities. Several theories can be found in published scientific literature to explain the seemingly unusual links between earthquakes and space phenomena.
However, traditional earthquake predictions are inadequate because of inaccuracies, inconsistencies, or insufficient lead time to allow for appropriate evacuations or other preparations that may be desired. With the increasing shift to population centers subject to earthquakes, the cost, problems, and opportunities to mitigate risks associated with earthquakes are expected to rise well into the future.